This post explains how a new qualitative protocol frames primary care physicians' views on large language model (LLM) adoption and what qualitative researchers should do next. The primary keyword is LLM adoption in primary care, and the target audience is qualitative researchers and health services teams designing studies or synthesis. According to PLOS One, the protocol was published on August 7, 2026 and describes a purposive, semi-structured interview study in British Columbia, Canada PLOS One. This post gives extractable methods, timelines, and direct quotes you can reuse in ethics applications and interview guides. Ethics note: this post is research-focused and not clinical advice.
Key Takeaways
The PLOS One protocol documents a descriptive qualitative study to explore physician views on LLMs, and it schedules recruitment from August 1, 2025 to January 31, 2026 PLOS One.
- 1) According to PLOS One, the article was published on August 7, 2026 and the manuscript was received July 18, 2025 and accepted July 22, 2026.
- 2) According to PLOS One, the study will purposively sample physicians by LLM experience using thresholds of 50+, 5–50, and fewer than 5 lifetime uses in clinical contexts.
- 3) According to PLOS One, the protocol reports that the number of Canadians without a family doctor rose from 4.5 million in 2019 to about 6.5 million in 2023, motivating a primary care focus.
- 4) According to Pokharel et al., the study will combine deductive and inductive thematic analysis under the new TAB framework to explain technology uptake.
What happened and how the study works: LLM adoption in primary care study
The protocol defines a descriptive qualitative study to capture family physicians’ perceptions of LLM adoption, according to PLOS One.
According to PLOS One, the research team will use semi-structured interviews with purposive sampling among British Columbia family physicians to represent varying levels of LLM use and years of practice.
According to PLOS One, interviewers will record, transcribe verbatim, deidentify, and analyze transcripts with NVivo using combined deductive codes from the TAB framework and inductive codes developed from the data.
According to Pokharel et al., "we plan to use semi-structured interviews with purposively sampled primary care physicians from British Columbia, Canada, " and the team will prepare reflexive memos to contextualize coding decisions.
Findings snapshot
| Date / Timeline | Metric | Value reported in protocol | Implication for qualitative teams |
|---|---|---|---|
| July 18, 2025 → July 22, 2026 | Manuscript lifecycle | Received July 18, 2025; Accepted July 22, 2026; Published August 7, 2026 | Use absolute dates when citing the protocol and pre-register timelines. |
| Aug 1, 2025 → Jan 31, 2026 | Anticipated recruitment window | 6 months planned for recruitment and data collection | Plan for rolling recruitment and interim pilot interviews as the protocol does. |
| 2019 → 2023 | Primary care access metric | Patients without family doctor rose from 4.5M in 2019 to ~6.5M in 2023 (Canada) | Frame participant sampling to include physicians serving high-need populations. |
| LLM exposure thresholds | Participant strata by prior LLM use | 50+ uses, 5–50 uses, <5 uses (in clinical context) | Predefine usage strata in screening surveys to ensure diversity of experience. |
Implications for qualitative researchers and health services teams
Answer: The protocol shows how to combine a theory-driven framework with flexible qualitative description to study LLM uptake, according to PLOS One.
According to PLOS One, the TAB framework unifies perception-focused models (perceived ease of use and usefulness) and task-technology fit traditions, so mixed deductive-inductive coding can map preexisting constructs and emergent clinician concerns.
Practical implication: According to PLOS One, purposive sampling by LLM experience and years in practice improves analytical generalizability; researchers should include screening items that record counts of prior LLM uses and practice tenure.
Practical implication: According to PLOS One, researchers should build reflexive memoing and monthly codebook review into their timeline to let a multidisciplinary team interrogate interpretations.
How Evidano helps: AI-enabled qualitative research for LLM adoption studies
What problem does a protocol like this create for researchers?
Answer: The protocol requires reproducible transcription, secure deidentification, rapid thematic synthesis, and transparent codebook management, according to PLOS One.
According to PLOS One, teams must record, transcribe, deidentify, and iteratively code interview data while preserving reflexive memos.
What Evidano does for those problems
Evidano is an AI-powered qualitative data analysis platform that helps researchers analyze interviews, open-ended surveys, and documents.
Evidano supports secure, encrypted transcription with custom dictionaries and PII redaction, meeting the protocol requirement to destroy original audio and work only with deidentified transcripts as described in PLOS One.
Evidano supports thematic and cross-segment analysis, so teams following the TAB framework can run deductive code frequency counts and inductive theme discovery, then export codebooks and reflexive memos for NVivo-compatible review; see Evidano's features page.
Evidano's transcription features specifically address the protocol's need to transcribe and deidentify interviews; see the speech-to-text page for details.
How to operationalize the protocol faster
Answer: Use Evidano to ingest audio, apply PII redaction, and run initial deductive code matching to the TAB framework before manual coder review.
According to PLOS One, the team plans to pilot the first two interviews to refine flow; Evidano can produce searchable transcripts and automated code suggestions that speed pilot analysis while preserving human-led reflexivity.
FAQ: LLM adoption in primary care
What is the study design in the PLOS One protocol?
Answer: The study is a descriptive qualitative design using semi-structured interviews, according to PLOS One.
According to PLOS One, the research team will use purposive sampling among British Columbia family physicians and combine deductive TAB-based coding with inductive theme development.
How many and which physicians will they recruit?
Answer: The protocol does not fix an explicit N; it aims for sample sufficiency by purposive sampling and variation, according to PLOS One.
According to PLOS One, the team will sample physicians with differing LLM exposure (50+ uses, 5–50 uses, and fewer than 5 uses) and vary years of practice to reach conceptual saturation.
What is the TAB framework and why use it?
Answer: TAB unifies perception-focused models and task-technology fit theories to explain adoption, according to PLOS One.
According to PLOS One, TAB maps perceived ease of use and usefulness into technology–task–user fit, enabling both hypothesis-driven and exploratory coding in thematic analysis.
Can I reuse interview language or the protocol timeline?
Answer: Yes, the protocol provides sample interview constructs and a planned timeline you can adapt, according to PLOS One.
According to PLOS One, the team will pilot the interview guide in the first two interviews and anticipates recruitment from August 1, 2025 to January 31, 2026, which you can cite with the publication date August 7, 2026.
How should teams handle consent, transcription, and PII per the protocol?
Answer: The protocol requires ethics-approved recruitment materials, informed consent, destruction of original audio after transcription, and working only with deidentified transcripts, according to PLOS One.
According to PLOS One, the study received ethics approval from the UBC Behavioral Research Ethics Board under protocol H25-01658 and will store and share deidentified data consistent with that approval.
Conclusion & Next Steps
The PLOS One protocol offers a replicable, theory-driven approach to studying LLM adoption in primary care and supplies timelines, sampling strata, and analysis plans you can adopt or adapt PLOS One.
Researchers running similar studies should predefine usage strata (the protocol uses 50+, 5–50, and <5 uses), build reflexive memoing into monthly codebook reviews, and plan transcription and PII workflows that match the ethics language in the protocol, according to PLOS One.
If you want to accelerate transcription, deidentification, and theory-aligned thematic analysis for a protocol like this, try Evidano’s integrated pipeline and collaborative codebook tools; see our features page for details.
Next step: register a trial dataset and pilot two interviews, then move from pilot to full recruitment following the protocol schedule and reporting standards in PLOS One.
Ready to run your interviews and analysis pipeline? Try Evidano for free.
Topics
- LLM adoption in primary care
- qualitative analysis of LLMs
- physician perceptions of AI
- AI-enabled qualitative research
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